HPC on Cloud: Results from the Record-breaking 156,000 Core MegaRun, and Insights to On-demand Clusters for Manufacturing Production Workloads

In case you missed this great session at the AWS re:Invent 2014 conference, Cycle CEO Jason Stowe hosted HGST (a Western Digital company), and University of Southern California (USC) to talk about HPC on the Cloud. See below for full session video, and abstract description.

BDT311 – HPC on AWS: Results from the Record-breaking 156,000 Core MegaRun, and Insights to On-demand Clusters for Manufacturing Production Workloads

Not only did the 156,000+ core run (nicknamed the MegaRun) on Amazon EC2 break industry records for size, scale, and power, but it also delivered real-world results. University of Southern California (USC) ran the high-performance computing job in the Cloud to evaluate over 220,000 compounds and build a better organic solar cell. In this session, USC’s Patrick Saris provides an update on the six promising compounds he found that he is now synthesizing in laboratories for his clean energy project. Saris discusses the implementation of and lessons learned in running a cluster in eight AWS regions worldwide, with highlights from Cycle Computing’s project Jupiter, a low-overhead cloud scheduler and workload manager. This session also looks at how the MegaRun was financially achievable using the Amazon EC2 Spot Instance market, including an in-depth discussion on leveraging Amazon EC2 Spot Instances to reduce costs and maximize value, while maintaining the needed flexibility, and agility that AWS is known for.

After a year of production workloads on AWS, HGST (a Western Digital company), has zeroed in on understanding how to create on-demand clusters to maximize value on AWS. HGST’s David Hinz outlines the company’s successes in addressing the company’s changes in operations, culture, and behavior to this new vision of on-demand clusters. In addition, the session will provide insights into leveraging Amazon EC2 Spot, versus Reserved Instances to maximize value.

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